hf test 1

README.md

@@ -1 +1,166 @@
-# Content-Based-Book-Recommender
+---
+title: Book Recommender
+emoji: ⚡
+colorFrom: indigo
+colorTo: gray
+sdk: gradio
+sdk_version: 5.6.0
+app_file: app.py
+pinned: false
+short_description: A content based book recommender.
+---
+
+# Content-Based-Book-Recommender
+A HyDE based approach for building recommendation engine.
+
+## Libraries installed separately
+
+I used google colab with following libraries extra. 
+
+NOT storing `requirements.txt` because issue while pushing to HF space.
+
+```SH
+pip install -U sentence-transformers datasets
+```
+
+## Training Steps
+
+**ALL files Paths are at set as CONST in beginning of each script, to make it easier while using the paths while inferencing; hence not passing as CLI arguments**
+
+### Step 1: Data Clean
+
+I am going to do basic steps like unwanted column removal (the first column of index), missing values removal (drop rows), duplicate rows removal. Output Screenshot attached.
+
+I am NOT doing any text pre-processing steps like stopword removal, stemming/lemmatization or special character removal because my approach is to use the casual language modelling (later steps) hence makes no sense to rip apart the word meaning via these word-based techniques.
+
+A little tinker in around with the dataset found that some titles can belong to multiple categories. (*this code I ran separately, is not part of any script*)
+
+![image](https://github.com/user-attachments/assets/cdf9141e-21f9-481a-8b09-913a0006db87)
+
+A descriptive analysis shows that there are just 1230 unique titles. (*this code I ran separately, is not part of any script*)
+
+![image](https://github.com/user-attachments/assets/072b4ed7-7a4d-48b2-a93c-7b08fc5bee45)
+
+We are not going to remove them rows that shows same titles (& summaries) with different categories but rather create a separate file for unique titles.
+
+```SH
+python z_clean_data.py
+```
+
+![image](https://github.com/user-attachments/assets/a466c20b-60ed-47ac-8bfc-e0a38ccdb88d)
+
+
+Output: `clean_books_summary.csv`, `unique_titles_books_summary.csv`
+
+
+### Step 2: Generate vectors of the books summaries.
+
+Here, I am going to use pretrained sentence encoder that will help get the meaning of the sentence. As the semantic meaning of the summaries themselves are not changed.
+
+We perform this over `unique_titles_books_summary.csv` dataset
+
+![image](https://github.com/user-attachments/assets/21d2d92b-0ad5-4686-8e38-c47df10893f8)
+
+Use command
+```SH
+python z_embedding.py
+```
+
+Just using CPU should take <1 min
+
+![image](https://github.com/user-attachments/assets/5765d586-cc50-4adf-b714-5e371f757f38)
+
+Output: `app_cache/summary_vectors.npy`
+
+### Step 3: Fine-tune GPT-2 to Hallucinate but with some bounds.
+
+Lets address the **Hypothetical** part of HyDE approach. Its all about generating random summaries,in short hallucinating. While the **Document Extraction** (part of HyDE) is about using these hallucinated summaries to do semantic search on database. 
+
+Two very important reasons why to fine-tune GPT-2 
+1. We want it to hallucinate but withing boundaries i.e. speak words/ language that we have in books_summaries.csv NOT VERY DIFFERENT OUT OF WORLD LOGIC.
+
+2. Prompt Tune such that we can get consistent results. (Screenshot from https://huggingface.co/openai-community/gpt2); The screenshot show the model is mildly consistent.
+
+  ![image](https://github.com/user-attachments/assets/1b974da8-799b-48b8-8df7-be17a612f666)
+  
+  > we are going to use ``clean_books_summary.csv` dataset in this training to align with the prompt of ingesting different genre.
+   
+Reference: 
+- HyDE Approach, Precise Zero-Shot Dense Retrieval without Relevance Labels https://arxiv.org/pdf/2212.10496
+- Prompt design and book summary idea I borrowed from https://github.com/pranavpsv/Genre-Based-Story-Generator
+  - I didnt not use his model
+      - its lacks most of the categories; (our dataset is different)
+      - His code base is too much, can edit it but not worth the effort.
+- Fine-tuning code instructions are from https://huggingface.co/docs/transformers/en/tasks/language_modeling
+
+Command
+
+You must supply your token from huggingface, required to push model to HF
+
+```SH
+huggingface-cli login
+```
+
+We are going to use dataset `clean_books_summary.csv` while triggering this training.
+
+```SH
+python z_finetune_gpt.py
+```
+(Training lasts ~30 mins for 10 epochs with T4 GPU)
+
+![image](https://github.com/user-attachments/assets/46253d48-903a-4977-b3f5-39ea1e6a6fd6)
+
+
+The loss you see is cross-entryopy loss; as ref in the fine-tuning instructions (see above reference) states : `Transformers models all have a default task-relevant loss function, so you don’t need to specify one `
+
+![image](https://github.com/user-attachments/assets/13e9b868-6352-490c-9803-c5e49f8e8ae8)
+
+So all we care is lower the value better is the model trained :) 
+
+We are NOT going to test this unit model for some test dataset as the model is already proven (its GPT-2 duh!!).
+But **we are going to evaluate our HyDE approach end-2-end next to ensure sanity of the approach**.
+
+## Evaluation
+
+Before discussing evaluation metric let me walk you through two important pieces recommendation generation and similarity matching;
+
+### Recommendation Generation
+
+The generation is handled by script `z_hypothetical_summary.py`. Under the hood following happens
+
+![image](https://github.com/user-attachments/assets/ee174c38-a1f3-438a-afb8-be2888c590da)
+
+Code Preview. I did the minimal post processing to chop of the `prompt` from the generated summaries before returning the result.
+
+![image](https://github.com/user-attachments/assets/132e84a7-cb4f-49d2-8457-ff473224bad6)
+
+### Similarity Matching 
+
+![image](https://github.com/user-attachments/assets/229ce58b-77cb-40b7-b033-c353ee41b0a6)
+
+![image](https://github.com/user-attachments/assets/58613cd7-0b73-4042-b98d-e6cdf2184c32)
+
+Because there are 1230 unique titles so we get the averaged similarity vector of same size.
+
+![image](https://github.com/user-attachments/assets/cc7b2164-a437-4517-8edb-cc0573c8a5e6)
+
+### Evaluation Metric
+
+So for given input title we can get rank (by desc order cosine similarity) of the store title. To evaluate we the entire approach we are going to use a modified version **Mean Reciprocal Rank (MRR)**.
+
+![image](https://github.com/user-attachments/assets/0cb8fc2a-8834-4cda-95d2-52a02ac9c11d)
+
+We are going to do this for random 30 samples and compute the mean of their reciprocal ranks. Ideally all the title should be ranked 1 and their MRR should be equal to 1. Closer to 1 is good.
+
+![image](https://github.com/user-attachments/assets/d2c77d47-9244-474a-a850-d31fb914c9ca)
+
+The values of TOP_P and TOP_K (i.e. token sampling for our generator model) are sent as `CONST` in the `z_evaluate.py`; The current set of values of this are borrowed from the work: https://www.kaggle.com/code/tuckerarrants/text-generation-with-huggingface-gpt2#Top-K-and-Top-P-Sampling
+
+MRR = 0.311 implies that there's a good change that the target book will be in rank (1/.311) ~ 3 (third rank) **i.e. within top 5 recommendations**
+
+
+
+
+
+
+

app.py

@@ -0,0 +1,36 @@
+# from z_utils import get_dataframe
+# import numpy as np
+
+# # CONST
+# SUMMARY_VECTORS = "app_cache/summary_vectors.npy"
+# BOOKS_CSV = "clean_books_summary.csv"
+
+# def get_recommendation(book_title: str) -> str:
+#     return book_title
+
+
+# def sanity_check():
+#     '''Validates whether the vectors count is of same as summaries present else RAISES Error
+#     '''
+#     global BOOKS_CSV, SUMMARY_VECTORS
+#     df = get_dataframe(BOOKS_CSV)
+#     vectors = np.load(SUMMARY_VECTORS)
+#     assert df.shape[0] == vectors.shape[0]
+    
+
+# Reference: https://huggingface.co/learn/nlp-course/en/chapter9/2
+
+import gradio as gr
+
+
+def greet(name):
+    return "Hello " + name
+
+
+# We instantiate the Textbox class
+textbox = gr.Textbox(label="Write truth you wana Know:", placeholder="John Doe", lines=2)
+
+
+demo = gr.Interface(fn=greet, inputs=textbox, outputs="text")
+
+demo.launch()